Training course: A/B testing & experimentation
What you need to know about A/B testing before you start
A/B testing requires a precise process and it takes a little time to familiarize yourself with its workings. In this article, we look at the basics and main principles you need to understand before you can run relevant experiments and create a successful A/B testing strategy.
Understanding the statistical value of an A/B test
Developing a solid understanding of the statistical significance indicators of your A/B tests is essential to successfully interpreting their results and to then improve your digital strategy. We explain how to understand and interpret the statistical significance of your A/B tests in this article.
Bayesian or frequentist statistics: which method should you use for your A/B tests?
When it comes to statistical significance, the A/B testing world is split into two approaches: Bayesian or frequentist statistics. What are the advantages and disadvantages of each approach? Do you really have to choose between the two? This article explains the debate and aims to answer these key questions.
A/A tests: how do they work?
A/A tests enable you to test two identical versions of an element. The traffic to your website is divided into two, with each group exposed to the same variation. Through this you will be able to determine whether the conversion rates in each group are similar and confirm that your solution is working properly.
What is Multivariate testing?
While multivariate testing is a very effective way to test and improve the experience, increasing conversion rates on your website, you need to follow best practice to get significant, reliable and actionable results. This blog explains what multivariate testing is, and outlines the methodology you need to follow to better engage with visitors on your website.
A/B testing: What traffic volumes do you need for reliable results?
It’s essential to allocate the right traffic volume to your A/B tests if you are to obtain reliable results. Depending on the conversion goals and the confidence levels you’ve set, the minimum traffic required for a significant experiment may vary. So, how should you calculate the traffic volumes necessary for obtaining statistically significant results from your A/B tests?
Dynamic traffic allocation: optimize your A/B tests
Dynamic traffic allocation (or multi-armed bandit testing) allows you to optimize your A/B tests by distributing the traffic on your website to the most efficient variations before the test phase has even finished. This post explains how the technique works and the benefits it delivers.
Are You Stopping Your A/B Tests Too Early?
Stopping A/B tests too early is without a doubt the most common—and one of the most dangerous - A/B testing mistakes. By finishing tests too soon your program may not deliver the benefits you are looking for. Even worse, as the decisions you make are based on invalid results they could negatively impact your conversion rates. In this blog we'll answer the following question: what concepts do I need to understand to avoid stopping my A/B tests too early?
Are You Misinterpreting Your A/B Test Results?
A/B testing is about learning, and making informed decisions based on your results. In this article we look at four errors that you should look to avoid when analysing the results of your A/B tests.
7 mistakes most beginners make when split testing
A staggering number of people doing split tests get imaginary results. How come? Because you can be mistaken in so many different ways, and because maths are involved. In this article, we’ll cover about every Split Testing mistakes you could make, and how to avoid them.
Client-side or server-side: which technical approach should you favor in your experimentation strategies?
Depending on company structure, internal resources and the complexity of your experiments, two technical approaches exist to running A/B tests: client-side and server-side. How do each of them work? What kinds of experiments are each best suited to? What are the benefits and limitations of each approach? Find the answers in this article.
How Intelligent Tracking Prevention (ITP) impacts A/B testing - and how marketers can overcome its restrictions
Can marketers trust their A/B testing tool anymore? That’s the key question given the latest updates to Apple’s latest Intelligent Tracking Prevention (ITP) technology, which is having an enormous impact on conversion rate optimization and experimentation practices. What is ITP, what are its main impacts on A/B testing tools and how can you overcome them?
Four steps to build your testing roadmap
If you want to run a successful experimentation program then optimizing your processes is just as important as the actual A/B tests that you run. Prioritizing your actions will allow you to get the best results from your testing program – therefore, in this article, we’ll outline the four essential steps to creating a successful A/B testing roadmap.
Checklist : How to choose your A/B testing solution
In this article, you’ll find a checklist of the main features brands should consider when choosing their testing solution. This will help you choose your A/B testing solution and find the right technology partner to support you in your digital conversion rate optimization projects, resulting in increases in online revenue and customer engagement.
A/B testing Certification
If you’ve been through our training course blogs and would like to learn more then Kameleoon provides an advanced course on A/B testing, including an online certification exam at the end.
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